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The construction of valid and flexible cross-covariance functions is a fundamental task for modeling multivariate space-time data arising from climatological and oceanographical phenomena. Indeed, a suitable specification of the covariance…

Statistics Theory · Mathematics 2017-11-23 Alfredo Alegría , Emilio Porcu , Reinhard Furrer , Jorge Mateu

We prove that a random distribution in two dimensions which is conformally invariant and satisfies a natural domain Markov property is a multiple of the Gaussian free field. This result holds subject only to a fourth moment assumption.

Probability · Mathematics 2020-04-24 Nathanael Berestycki , Ellen Powell , Gourab Ray

Iterative methods for fitting a Gaussian Random Field (GRF) model via maximum likelihood (ML) estimation requires solving a nonconvex optimization problem. The problem is aggravated for anisotropic GRFs where the number of covariance…

Machine Learning · Statistics 2021-01-12 Sam Davanloo Tajbakhsh , Necdet Serhat Aybat , Enrique Del Castillo

Gaussian random field on general ultrametric space is introduced as a solution of pseudodifferential stochastic equation. Covariation of the introduced random field is computed with the help of wavelet analysis on ultrametric spaces. Notion…

Probability · Mathematics 2011-05-10 A. Yu. Khrennikov , S. V. Kozyrev

We consider distributed consensus and vehicular formation control problems. Specifically we address the question of whether local feedback is sufficient to maintain coherence in large-scale networks subject to stochastic disturbances. We…

Optimization and Control · Mathematics 2013-04-23 Bassam Bamieh , Mihailo R. Jovanović , Partha Mitra , Stacy Patterson

This article provides an introduction to the asymptotic analysis of covariance parameter estimation for Gaussian processes. Maximum likelihood estimation is considered. The aim of this introduction is to be accessible to a wide audience and…

Statistics Theory · Mathematics 2020-09-16 François Bachoc

We study the problem of learning mixtures of $k$ Gaussians in $d$ dimensions. We make no separation assumptions on the underlying mixture components: we only require that the covariance matrices have bounded condition number and that the…

Data Structures and Algorithms · Computer Science 2024-11-20 Sitan Chen , Vasilis Kontonis , Kulin Shah

For many applications with multivariate data, random field models capturing departures from Gaussianity within realisations are appropriate. For this reason, we formulate a new class of multivariate non-Gaussian models based on systems of…

Methodology · Statistics 2020-01-01 David Bolin , Jonas Wallin

We consider the statistical experiment given by a sample of a stationary Gaussian process with an unknown smooth spectral density f. Asymptotic equivalence, in the sense of Le Cam's deficiency Delta-distance, to two Gaussian experiments…

Statistics Theory · Mathematics 2009-03-10 Georgi K. Golubev , Michael Nussbaum , Harrison H. Zhou

We investigate the conditions under which cosmological variations in physical `constants' and scalar fields are detectable on the surface of local gravitationally-bound systems, such as planets, in non-spherically symmetric background…

General Relativity and Quantum Cosmology · Physics 2011-07-19 Douglas J. Shaw , John D. Barrow

We consider the homogenization of parabolic equations with large spatially-dependent potentials modeled as Gaussian random fields. We derive the homogenized equations in the limit of vanishing correlation length of the random potential. We…

Mathematical Physics · Physics 2008-09-08 Guillaume Bal

In this work, we give efficient algorithms for privately estimating a Gaussian distribution in both pure and approximate differential privacy (DP) models with optimal dependence on the dimension in the sample complexity. In the pure DP…

Data Structures and Algorithms · Computer Science 2023-06-02 Daniel Alabi , Pravesh K. Kothari , Pranay Tankala , Prayaag Venkat , Fred Zhang

This work considers stationary vector count time series models defined via deterministic functions of a latent stationary vector Gaussian series. The construction is very general and ensures a pre-specified marginal distribution for the…

Statistics Theory · Mathematics 2023-10-31 Marie-Christine Düker , Robert Lund , Vladas Pipiras

We consider the problem of subspace estimation in situations where the number of available snapshots and the observation dimension are comparable in magnitude. In this context, traditional subspace methods tend to fail because the…

Information Theory · Computer Science 2016-11-15 Pascal Vallet , Philippe Loubaton , Xavier Mestre

We study the variance of the number of zeroes of a stationary Gaussian process on a long interval. We give a simple asymptotic description under mild mixing conditions. This allows us to characterise minimal and maximal growth. We show that…

Probability · Mathematics 2022-05-25 Eran Assaf , Jeremiah Buckley , Naomi Feldheim

To investigate and specify the statistical properties of cosmological fields with particular attention to possible non-Gaussian features, accurate formulae for the bispectrum and the bispectrum covariance are required. The bispectrum is the…

Cosmology and Nongalactic Astrophysics · Physics 2015-05-30 Sandra Martin , Peter Schneider , Patrick Simon

Analytical templates for the covariance matrix of the 4-Point Correlation Function (4PCF) have been developed in the past assuming a Gaussian Random Field (GRF). In this work, we present the first non-Gaussian calculation of the 4PCF…

Cosmology and Nongalactic Astrophysics · Physics 2025-09-09 William Ortolá Leonard , Zachary Slepian

For a stationary random function $\xi$, sampled on a subset $D$ of $\mathbb{R}^{d}$, we examine the equivalence and orthogonality of two zero-mean Gaussian measures $\mathbb{P}_{1}$ and $\mathbb{P}_{2}$ associated with $\xi$. We give the…

Probability · Mathematics 2024-04-23 Reinhard Furrer , Michael Hediger

We theoretically analyze the properties of a geodesic random walk on the Euclidean $d$-sphere. Specifically, we prove that the random walk's transition kernel is Wasserstein contractive with a contraction rate which can be bounded from…

Statistics Theory · Mathematics 2024-10-15 Philip Schär , Thilo D. Stier

While any symmetric and positive semidefinite mapping can be the non-centered covariance of a Gaussian random field, it is known that these conditions are no longer sufficient when the random field is valued in a two-point set. The question…

Probability · Mathematics 2026-02-04 Xavier Emery , Christian Lantuéjoul
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